Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow

See discussions, stats, and author profiles for this publication at: http://www.researchgate.net/publication/266391703 Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow CONFERENCE PAPER in TRANSPORTATION RESEARCH RECORD JOURNAL OF THE TRANSPORTATION RESEARCH BOARD JANUARY 2012 Impact Factor: 0.44 DOI: 10.3141/2324-08 CITATIONS 13 DOWNLOADS 146 VIEWS 118 3 AUTHORS, INCLUDING: Steven E. Shladover University of California, Berkeley 127 PUBLICATIONS 1,516 CITATIONS Xiao-Yun Lu University of California, Berkeley 121 PUBLICATIONS 567 CITATIONS SEE PROFILE SEE PROFILE Available from: Xiao-Yun Lu Retrieved on: 13 September 2015

TRB 12-1868 Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow Steven E. Shladover (Corresponding Author) California PATH Program, University of California, Berkeley 1357 S. 46th Street, Richmond Field Station, Bldg 452, Richmond, CA 94804-4648 Tel: 1-510-665-3514, Fax: 1-510-665-3537, Email: steve@path.berkeley.edu Dongyan Su California PATH Program, University of California, Berkeley 1357 S. 46th Street, Richmond Field Station, Bldg 452, Richmond, CA 94804-4648 Tel: 1-510-928-8771, Email: dongyan@berkeley.edu Xiao-Yun Lu California PATH Program, University of California, Berkeley 1357 S. 46th Street, Richmond Field Station, Bldg 452, Richmond, CA 94804-4648 Tel: 1-510-665-3644, Fax: 1-510-665-3691, Email: xylu@path.berkeley.edu For presentation and publication at 91st TRB Annual Meeting Washington, D.C. Jan. 22-26, 2012 Originally Submitted: July 31, 2011 Words: 5122 Figures: 4 x 250 = 1000 Tables: 3 x 250 = 750 Total Word Count: 6872 Revised version submitted: November 11, 2011 1

Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow ABSTRACT This paper describes the use of microscopic simulation to estimate the effect of varying market penetrations of adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC) on highway capacity. The distribution of time gap settings that drivers from the general public used in a real field experiment were used in the simulation, making this the first study of the effects of ACC and CACC on traffic to be based on real data on driver usage of ACC and CACC. The results show that the use of ACC is unlikely to change lane capacity significantly. However, CACC is able to greatly increase capacity after its market penetration reaches moderate to high percentages. The capacity increase can be accelerated by equipping non-acc vehicles with Vehicle Awareness Devices so that they can serve as the lead vehicles for CACC vehicles. 1. INTRODUCTION An earlier phase of this project included a field test of Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), driven by 16 drivers from the general public. Those drivers were encouraged to select the time gap settings that they preferred for each system, and their selections of time gap were recorded, along with many other parameters, for subsequent analysis. They were also surveyed to determine their subjective opinions about the ACC and CACC systems. The results of this experiment were very encouraging about the potential market acceptance of ACC and CACC when they are made available to the general public. These results were reported in two prior project reports (1, 2) and two technical papers (3, 4). The C/ACC field test produced quantitative results indicating the relative preferences of the driving population for driving at the different available time gap settings. These time gap preferences can have a significant influence on traffic flow and highway lane capacity. The work reported here uses a traffic microsimulation, based on the C/ACC field test results, to produce the first authoritative quantitative estimates of the impacts that these systems could have on highway capacity. The maximum traffic flow is determined by admissible time gaps between vehicles. The admissible time gap is determined by the means of controlling the vehicle s following: manual driving, ACC, or CACC. In manual driving, the acceptable time gap is determined based on the driver s perception of what is safe, including his or her perception and reaction time, and is influenced by the driver s experiences, including expectations about the behaviors of other drivers, especially the driver of the leading vehicle. Vehicles with ACC or CACC have discrete time gap settings, which the driver can select based on his or her perceptions of the capabilities of the system. The net effect on traffic depends on whether drivers have 2

sufficient confidence in the C/ACC systems to select time gaps that differ significantly from the gaps they use in manual driving. This research evaluates the effects of the use of ACC and CACC on freeway capacity by microscopic simulation, based on the actual gaps that the drivers selected in our field testing of these systems. The simulation platform for this study is the commercially available traffic microsimulation program Aimsun, which was selected because it was the only simulation platform in which we could implement the NGSIM over-saturated freeway flow model, to provide the most realistic representation of normal drivers car following behavior in dense traffic. Our field test of CACC (1-4) was motivated by a simulation study that we conducted ten years ago, which showed that if drivers would be comfortable using CACC at a time gap of 0.5 s, the capacity per lane could be increased to as high as 4400 vehicles per hour if all vehicles were equipped with CACC (5, 6). However, the field study was needed to determine which gaps the drivers would find acceptable. When we did the original simulation study (5, 6), there were already several published papers using a variety of modeling approaches to estimate the highway capacity implications of ACC, producing widely varying estimates of capacity because of significantly different assumptions about the car following behavior of drivers and ACC systems. Those earlier studies were already reviewed in (5, 6). During the past ten years there have been a few additional studies estimating the impacts that ACC and CACC could have on traffic flow, but none have yet had the benefit of real experimental data about how drivers actually use these systems on the highway. van Arem et al (7) used the MIXIC microscopic simulation to investigate the traffic throughput and stability impacts of CACC, incorporating good vehicle dynamics and driver behavior models. They studied a freeway lane drop as the disturbance to induce a shockwave to limit capacity, and found that the shockwave effect could be mitigated and the average speed increased with higher market penetrations of CACC. Their predictions of CACC impacts are generally consistent with the results that we will show here, with significant capacity increases at the higher market penetration levels. Kesting, et. al (8, 9) simulated ACC with infrastructure-determined set speeds, a form of infrastructure-cooperative ACC rather than vehicle-cooperative ACC. Their IDM model showed that a 25% market penetration of these ACC vehicles could eliminate congestion (and even 5% could produce noticeable improvements in travel times) for the specific peak-period traffic scenario that they chose to simulate, but this congestion benefit appears to be attributable to the variable speed limit strategy that they adopted rather than to the carfollowing dynamics of the ACC system. They did not directly consider the impacts that ACC would have on the achievable capacity. Schakel et al (10) used a modified version of IDM (which they called IDM+) to explore the traffic flow stability implications of CACC and an Acceleration Advice Controller (AAC), which advises the driver when to accelerate and decelerate rather than doing so automatically. They included results of a field experiment using 50 vehicles equipped with the AAC, showing reductions in variability of speeds and gaps between vehicles. Their CACC design was focused on improving traffic flow stability rather than on increasing capacity, so they did not directly address the capacity issue. 3

Kesting et al (11) hypothesized an idealized car following model designed to dampen traffic disturbances, and then assumed that this would represent ACC driving, even though it does not correspond to real ACC system behavior. They simulated combinations of vehicles represented by this model with other vehicles driven by their IDM car following model (which incorporates unrealistically hard braking in response to cut-in vehicles), and assumed that that model would represent the conventionally driven vehicles. As they increased the market penetration of the idealized ACC vehicles in their simulations, they showed significant increases in bottleneck traffic flow compared to the case with all vehicles driven by the IDM model, but both of their car following models were sufficiently unrealistic that this sheds little light on the flow rates that could be achieved with or without ACC. This paper is organized as follows: Section 2 describes the vehicle types used in the simulation; Section 3 describes the simulation platform; Sections 4 and 5 describe the dynamics of the ACC/CACC and manual driving models, respectively; Section 6 covers the simulation set up; Section 7 shows the results of the simulation; and Section 8 provides conclusions. 2. VEHICLE TYPES TO BE SIMULATED There are four vehicle types represented in the simulation, to accommodate all possible combinations of vehicles that could be interacting with each other in ways that would influence freeway traffic flow and capacity: (a) Manual vehicle - driven manually by a driver, with car following behavior represented by the NGSIM oversaturated flow model (b) Adaptive cruise control (ACC) - car following is determined based on a simple firstorder control law representing the behavior of a typical ACC system, with relatively slow, gentle responses to changes by the car ahead. (c) Here I am! (HIA) - driven manually just like the Manual vehicle, but it is equipped with a DSRC radio that frequently broadcasts a here I am message giving its location and speed. If it is being followed by a CACC vehicle, that following vehicle can use CACC. (d) Cooperative adaptive cruise control (CACC) - if it is following an HIA vehicle or another CACC vehicle, it can use its CACC car-following capability. If it is following a manual vehicle or an ACC vehicle, it acts like another ACC vehicle. The CACC car-following capability includes a faster response to changes by the car ahead and permits following at significantly shorter time gaps, based on the gap values chosen by drivers in our field test (2-4). Differences in the behavior of car following models account in large part for the wide range of effects that have been predicted in previous studies of the effects of ACC on traffic flow. The models of conventional car following are still not fully matured, but we have chosen a 4

state of the art model that has been calibrated based on real microscopic freeway traffic data, as explained in Section 5 of this paper. ACC systems developed by different manufacturers display a considerable variety of performance characteristics, based on the limitations of their sensors and on the different performance objectives their designers have adopted in trying to match them to the personalities and market segments of their host vehicles. The detailed carfollowing logics of these systems are proprietary to their developers, representing valuable intellectual property, so these are not publicly available. Some of them drive like aggressive drivers and some like timid drivers there is no uniform standard for their dynamic response. Indeed, recent tests of ACC systems from five Japanese auto companies (unfortunately not yet documented in the technical literature, but presented informally in a panel session at the 2011 ITS World Congress) have shown this diversity of dynamic responses quantitatively and have indicated potential challenges when vehicles with incompatible car-following behavior are driven one behind the other. Considering this diversity of ACC behavior, and the shortage of public documentation of the car-following dynamics of real ACC vehicles, we have chosen to adopt a simple first-order dynamic response for ACC car following. From a vehicle dynamics perspective, this could be considered somewhat optimistic because it is more stable than the actual responses of ACC systems, which have substantial transport lags (in the range of a half to a full second) associated with the limitations of their sensor signal processing systems. Our analysis concentrates on steady-state car following behavior rather than on the dynamics associated with large speed changes in bottleneck conditions because in those more severe conditions it is generally necessary for the driver to intervene and take over control from the ACC, which makes the combined ACC and driver dynamics extremely complicated and diverse. The prior simulation studies that have attempted to represent ACC performance in bottlenecks have ignored this problem and have instead assumed ACC systems to be braking harder than any real ACC systems can brake (twice as hard as the maximum ACC braking permitted by ISO 15622 in the case of Reference 11). 3. THE MICROSIMULATION PLATFORM The simulation has been built on Aimsun (12), which contains microscopic and mesoscopic simulators, a dynamic traffic simulator, and macroscopic and static assignment models. It also offers extended tools for advanced investigation, like the Aimsun SDK (Software Development Kit), Aimsun API, and Aimsun MicroSDK. We used the Aimsun Microscopic Simulator, API, and MicroSDK in this project. The Aimsun Microscopic Simulator (13) is the tool to construct traffic networks, define vehicle types and their basic properties, specify traffic demand and traffic control, and run the simulations. For this project, the geometry of the freeway and its speed limit, the four vehicle types and their properties, such as length and width, are defined in the Aimsun Microscopic Simulator. The Aimsun API module (14) is an interface that allows external applications to exchange data with Aimsun during simulation. The user can obtain necessary information during 5

simulation, such as the measurements of a detector or the state of a particular vehicle. The user can also control the traffic, determining when and how a new vehicle enters the network. The Microscopic Model SDK (15) is the tool to implement new behavior models in the Aimsun simulation, replacing the default driver model in Aimsun. The new driver behavior is implemented by the plug-in, which is a DLL file generated after building some C++ files. During each simulation step, Aimsun calls the functions in the plug-in and updates the driver behavior based on the user-defined model. The new driver behavior models that we developed to represent manual driving, ACC and CACC vehicles were programmed in MicroSDK. 4. CONTROL ALGORITHMS FOR ACC/CACC VEHICLES The following variables are used to define the vehicle-following control algorithms for ACC or CACC vehicles in this section. It should be noted that these are simplified representations of the ACC and CACC car-following rules that were actually implemented on the test vehicles for the field experiments. The ACC car-following rules are proprietary to Nissan, while the CACC car following behavior has been described in (16). Simpler representations were needed here for computational efficiency, because they need to be executed many times in each simulation, and also because the finer details of the actual car following dynamics of these systems were implemented for driver comfort but probably have little influence on traffic flow dynamics. v speed of the controlled ACC/CACC vehicle (m/s). v d desired speed set by the driver, or the speed limit of the road (m/s). v e speed error (m/s). a sc acceleration by speed control (m/s 2 ). s spacing between the controlled vehicle and its leading vehicle (m). s d desired spacing (m). s spacing error (m). e T desired time gap (s). d ACC and CACC vehicles have very similar control algorithms, with the difference being in their desired time gaps. There are two modes, speed control and gap control, in the ACC/CACC control algorithm. The goal of speed control is to keep the vehicle speed close to the speed limit, and that of gap control is to maintain the gap between the controlled vehicle and its leading vehicle to be the desired gap. Speed control is activated when the spacing to the preceding vehicle in the same lane is larger than 120 meters, and gap control is activated when the spacing is smaller than 100 meters. If the spacing is between 100 meters and 120 meters, the controlled vehicle retains the previous control strategy to provide hysteresis to avoid dithering between the two strategies. These fixed distances for transitions are appropriate for operations at or near full highway speed, but for lower speed operations these distances should be smaller. 6

In speed control, the control law is v a e sc v v a a sc d (4.1) ( e (4.2) (4.3) bound 0.4 v,2, 2) where the function bound () is defined as bound( x, xub, xlb ) : max(min( x, xub ), xlb ). The values +2 and -2 in (4.2) are the maximum acceleration and deceleration of the vehicle under 2 C/ACC control in units of m/ s. This control law tries to eliminate the error between the vehicle speed and the ACC set speed if the vehicle is in the speed control mode, with a time constant of 2.5 s, representing a typical ACC response speed. In gap control, the control law is based on maintaining a constant time gap between vehicles, which is typical of ACC vehicle following strategies and well accepted by drivers because of its general similarity to drivers own vehicle following behavior: v v v e d a bound( 0.4 v,2, 2) sc s T v d e d s s s d a bound( s 0.25 s, a, 2) e e sc (4.4) (4.5) (4.6) (4.7) (4.8) The +2 and -2 in (4.5) and (4.8) have the same meaning as in speed control. This control law forces the vehicle to approach its desired time gap set point in gap control. But the vehicle will still obey the speed limit in gap control, because if the commanded vehicle speed is larger than the set speed, this control law abides by the set speed, even if the current gap is larger than its desired gap. 5. MANUAL DRIVING MODEL The manual driver behavior model is the NGSIM oversaturated freeway flow model developed by Yeo et al (17, 18). The NGSIM oversaturated freeway flow model contains carfollowing and lane-changing models, but we only use the car-following model, the CF mode in (17, 18), because there is no lane changing in this simulation. The following variables are used in the manual driving model: U L x n and x n upper bound and lower bounds for the clearance between vehicles (m). n vehicle sequence ID w T kinematic wave travel time (s). jam g n jam gap (m). l length of vehicle (m). a acceleration of vehicle (m/s 2 ). 7

v speed of vehicle (m/s). x position of vehicle (m). f v free flow speed (m/s). The basic car-following model comes from Newell's linear model. It can be described as follows: U L x ( t t) max{ x ( t t), x ( t t)} n n n U w jam U 2 f s n ( ) min{ n 1( n ) n 1 n, n( ) n( ) n, n( ) n, n( ) n( )} x t t x t t T l g x t v t t a t x t v t x t x t t L L 2 n( ) max{ n() n() n, n()} x t t x t v t t a t x t x t t t at at a x d s L w L w 2 L n( ) n n ( n n ) 2 n( n 1 v () t 2 () t 2 n 1 L an 1 () t x () t ( l g ) d () t jam n 1 n n 1 n n 1 (5.1) (5.2) (5.3) (5.4) (5.5) The kinematic wave travel time in congested traffic conditions, T w is equivalent to the ACC time gap, with an offset to account for the minimum spacing at jam density. This NGSIM oversaturated freeway flow model was calibrated by using the NGSIM data (19). 6. A FREEWAY SECTION MODEL WITH SIMPLIFIED ROAD GEOMETRY The simulated road is a one-lane straight freeway with speed limit of 105 km/h (65 mph). The freeway is 6.5 km long, and there is a detector 6 km from the entrance. This location is selected to make sure all the flow measurements are in steady state. The freeway is empty before the simulation. During the simulation, the entering of new vehicles is controlled by an algorithm written in the API file, which will be described subsequently. The total simulation length is 1 hour, and the simulation step is 0.1 second. The flow is recorded at intervals of 5 minutes, but the first measurement is discarded because the first 5 minutes are viewed as the warm-up time. The capacity is the average flow over the remaining 55 minutes. The four types of vehicles have the same physical characteristics, with a length of 4.7 meters and width 1.9 meters. The accelerations are bounded within ± 2 m/s 2. In the simulation, the type of the next entering vehicle is randomly chosen, but follows the percentages defined in the simulation cases we want to test. The desired time gap of the entering vehicle is also random. For manual driving, the randomness is introduced by the randomness of jam n g. We know that the maximum flow for manually driven vehicles on this type of simple freeway link should be about 2200 veh/h, so we assume the desired headway for manual driving is 1.64 sec ( 3600/2200). The desired time gaps of the ACC or CACC vehicles were selected based on the gaps actually selected by drivers in the field test (1-4), to be: 8

ACC: 31.1% at 2.2 s, 18.5% at 1.6 s, 50.4% at 1.1 s CACC: 12% at 1.1 s, 7% at 0.9 s, 24% at 0.7 s, 57% at 0.6 s. Note that the difference between headway and time gap needs to be accounted for by incorporating the incremental time needed to travel the vehicle length at the defined operating speed. The desired entering headways for the ACC and CACC vehicles are chosen based on these time gaps, with the addition of the time increment to account for vehicle length. The gap for manual driving is selected randomly during the simulation, within a +/- 10% error range of 1.64 s, that is, from 1.48 to 1.8 sec. At each simulation step, we check the travel time from the entrance to the location of the last entering vehicle, based on the speed of that vehicle at that step. If this travel time is larger than the desired entering time gap, we let a new vehicle enter the freeway at the same speed as its leading vehicle at that step. By this algorithm, the vehicles enter the freeway at an interval and speed that will not generate a measured maximum flow lower than the real capacity due to insufficient demand, while preventing collisions associated with entering at too high a speed or too small a time gap. Because the entering time gap for manually driven vehicles usually does not match their desired time gap, the manually driven vehicles need to adjust their speeds after they enter the freeway. This causes the vehicles following them, whether they are manual, ACC or CACC, to also need to adjust speeds. By this, we introduce small disturbances into the simulation. This means that the simulated maximum flows should be achievable and stable in traffic with small disturbances. 7. Simulation Scenarios and Results Simulation scenarios have been defined to represent diverse combinations of manually driven, ACC, CACC and HIA vehicles so that the effects of changes in market penetration of each kind of vehicle can be determined. For each scenario, three simulations were run with different random number seeds and the results of those simulations were averaged to produce the estimates of achievable traffic flow. The all-manual case was already referenced as a base case with a nominal capacity that could potentially approach 2200 veh/hr per lane. Allowing for the disturbances in vehicle motions and the diversity of driver gap selections, the simulations produced an average capacity of 2018 veh/hr per lane with all manual driving. When basic ACC vehicles were incorporated into the traffic stream, the achievable traffic flow appeared to be remarkably insensitive to the market penetration of ACC vehicles, with flow remaining within the narrow range from 2030 to 2100 vehicles per hour regardless of the market penetration. This is a consequence of the driver preferences for ACC time gap settings being similar to the time gaps that they adopt when they drive manually. It is important to note that this contradicts the results in some published papers (8, 11) that contend that ACC could substantially increase highway capacity. 9

If we consider only the combinations of manually driven and CACC vehicles, the trend in highway lane capacity with respect to CACC market penetration is as shown in the lower part of the histograms in Figure 1. This has a quadratic shape, based on the fact that the CACC vehicle can only use its CACC capability when it is following another CACC vehicle, but when it is following a manual vehicle it must revert to conventional ACC control. As a result of this, the capacity grows slowly until the CACC market penetration becomes substantial, and then it grows more rapidly. If all vehicles in a lane were equipped with CACC capability and the drivers chose the same distribution of CACC time gaps as they chose in our field test, the lane capacity would increase to 3970 vehicles per hour, a very dramatic improvement. Figure 1. Highway Lane Capacity (Vehicles/Hour) as a Function of Changes in CACC Market Penetration Relative to Manually Driven Vehicles or Vehicles with Vehicle Awareness Devices One of the strategies being proposed in the Connected Vehicles initiative to improve performance of cooperative systems at low market penetrations is to equip as many existing vehicles as possible with a simple and inexpensive aftermarket positioning and communication Onboard Unit (OBU), called a Vehicle Awareness Device (VAD), that can broadcast a Here I Am (HIA) message. This message provides the basic GPS coordinates and vehicle speed and heading information so that the OBUs on other vehicles can detect the trajectory of the vehicle. This information, if it is sufficiently accurate, would enable a VAD equipped vehicle to be the leader for a CACC vehicle to follow at a short time gap. The 10

effects of replacing the manually driven vehicles with VAD vehicles are shown in the upper segments of the histograms of Figure 1. In this case, all the vehicles that do not have CACC are equipped with the VAD and can therefore serve as leaders for the CACC vehicles. With this change, the quadratic growth becomes more nearly linear, and the capacity of the highway lane can be increased more significantly even at modest CACC market penetrations. At a 20% market penetration, the HIA addition increases capacity by 7%, at 30% market penetration it increases by more than 10% and in the 50% to 60% market penetration range the increase is in the range of 15% compared to the cases without VADs. In our earlier studies of CACC, prior to the current project, we simulated the effects of the different combinations of ACC and CACC market penetrations, based on the assumption that the CACC vehicles would be driven at 0.5 s time gaps (5, 6). This produced a 3-D plot of achievable highway lane capacity that is reproduced here as Figure 2. The new simulation results, based on the time gaps that drivers actually chose in our field test, are shown in Figure 3 and Table 1. These capacity estimates are somewhat lower, with the 80% CACC/20% ACC result now in the range of 3000 rather than 3500 vehicles per hour, for example. Figure 2 Original Prediction of Lane Capacity Effects of ACC and CACC Driven at 0.5 s Time Gap from 2001 (5, 6) 11

Figure 3 Updated Prediction of Lane Capacity Effects of ACC and CACC Driven at Time Gaps Chosen by Drivers in Field Test (With the remaining vehicles manually driven) Table 1 Updated Prediction of Lane Capacity Effects of ACC and CACC Driven at Time Gaps Chosen by Drivers in Field Test (With the remaining vehicles manually driven) Percentage of ACC Percentage of CACC Vehicles 10% 20% 30% 40% 50% 60% 70% 80% 90% 10% 2065 2090 2170 2265 2389 2458 2662 2963 3389 20% 2065 2110 2179 2265 2378 2456 2671 2977 0 30% 2077 2127 2179 2269 2384 2487 2710 0 0 40% 2088 2128 2192 2273 2314 2522 0 0 0 50% 2095 2133 2188 2230 2365 0 0 0 0 60% 2101 2138 2136 2231 0 0 0 0 0 70% 2110 2084 2155 0 0 0 0 0 0 80% 2087 2101 0 0 0 0 0 0 0 90% 2068 0 0 0 0 0 0 0 0 The capacity effects of different combination of CACC vehicles and VAD vehicles (with the rest being manually driven) are shown in Figure 4 and Table 2. As the market penetration of CACC increases, the increasing capacity attributable to the additional VAD vehicles can be seen, but it is a relatively subtle effect. For completeness, the analogous results for different combinations of CACC vehicles and VAD vehicles (with the rest being conventional ACC 12

vehicles) are shown in Table 3. Since the effects on capacity of ACC and manually driven vehicles are very similar, these results do not differ much from the previous results. Figure 4 Prediction of Lane Capacity Effects of VAD (HIA) and CACC Driven at Time Gaps Chosen by Drivers in Field Test (With the remaining vehicles manually driven) Table 2 Prediction of Lane Capacity Effects of VAD and CACC Driven at Time Gaps Chosen by Drivers in Field Test (With the remaining vehicles manually driven) Percentage of VAD Percentage of CACC Vehicles 10% 20% 30% 40% 50% 60% 70% 80% 90% 10% 2045 2110 2179 2288 2447 2576 2760 3111 3624 20% 2054 2125 2211 2323 2512 2671 2893 3303 0 30% 2064 2148 2246 2378 2519 2787 3041 0 0 40% 2073 2165 2282 2434 2611 2891 0 0 0 50% 2084 2187 2318 2503 2685 0 0 0 0 60% 2097 2206 2362 2545 0 0 0 0 0 70% 2102 2227 2395 0 0 0 0 0 0 80% 2114 2252 0 0 0 0 0 0 0 90% 2123 0 0 0 0 0 0 0 0 Table 3 Prediction of Lane Capacity Effects of VAD and CACC Vehicles Driven at Time Gaps Chosen by Drivers in Field Test (With the remaining vehicles being ACC) 13

Percentage of VAD Percentage of CACC Vehicles 10% 20% 30% 40% 50% 60% 70% 80% 90% 10% 2086 2132 2168 2278 2443 2567 2831 3108 3624 20% 2135 2164 2207 2366 2446 2669 2941 3303 0 30% 2137 2193 2291 2364 2533 2775 3041 0 0 40% 2128 2206 2302 2439 2588 2891 0 0 0 50% 2139 2220 2324 2499 2685 0 0 0 0 60% 2134 2239 2373 2545 0 0 0 0 0 70% 2137 2245 2395 0 0 0 0 0 0 80% 2132 2252 0 0 0 0 0 0 0 90% 2123 0 0 0 0 0 0 0 0 8. CONCLUDING REMARKS The results reported here represent the first predictions of the effects of ACC and CACC on highway lane capacity that are founded on real experimental data, from drivers who have driven the suitably equipped vehicles and selected the time gap settings with which they were comfortable. These results show that conventional ACC is unlikely to produce any significant change in the capacity of highways because drivers are only comfortable using the ACC system at gap settings similar to the gaps they choose when driving manually. In contrast, CACC has the potential to substantially increase highway capacity when it reaches a moderate to high market penetration because its higher dynamic response capabilities give drivers confidence that it can follow safely at significantly shorter gap settings, so they actually select those shorter gaps. These results showed a maximum lane capacity of about 4000 vehicles per hour if all vehicles were equipped with CACC. If the vehicle population consists of CACC and VAD vehicles, meaning that all vehicles have been equipped with DSRC radios, the lane capacity increases approximately linearly from 2000 to 4000 as the percentage of CACC vehicles increases from zero to one hundred. On the other hand, if the vehicle population consists of manual and CACC vehicles, without any mandate for non-cacc vehicles to be equipped with DSRC, the increase in lane capacity follows a quadratic profile, lagging significantly behind at the intermediate market penetration values. Therefore, the capacity benefits of CACC can be accelerated, or obtained at somewhat lower market penetrations, if the rest of the vehicle population is equipped with Vehicle Awareness Devices so that they can serve as the lead vehicles for the CACC vehicles. Further work will be needed to extend the models of ACC performance to include the full complexity of the dynamic response of real ACC systems, to represent their responses to strong traffic disturbances and to show the ability of the combined ACC and driver system to 14

respond safely to emergency stopping conditions even when driven at the short gaps enabled by CACC (which has already been demonstrated on test tracks). ACKNOWLEDGMENTS This research has been conducted under the sponsorship of the State of California, Business, Transportation and Housing Agency, Department of Transportation (Caltrans) and the Federal Highway Administration s Exploratory Advanced Research Program, with additional support from Nissan Technical Center North America. The contents of this paper reflect the views of the authors, who are responsible for the facts and accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. REFERENCES 1. Shladover, S. E., X.-Y. Lu, D. Cody, C. Nowakowski, Z. Qiu, A. Chow, J. O Connell, J. Nienhuis and D. Su, Development and Evaluations of Selected Mobility Applications for VII, PATH Research Report UCB-ITS-PRR-2010-25. Available online at: http://database.path.berkeley.edu/reports/index.cgi?reqtype=displayrecord&record=1011 2. Nowakowski, C., S. E. Shladover, D. Cody, F. Bu, J. O Connell, J. Spring, S. Dickey, and D. Nelson, Cooperative Adaptive Cruise Control: Testing Drivers Choices of Following Distances, PATH Research Report UCB-ITS-PRR-2011-01. Available online at: http://database.path.berkeley.edu/reports/index.cgi?reqtype=displayrecord&record=1032 3. Steven E. Shladover, Christopher Nowakowski, Jessica O Connell and Delphine Cody, Cooperative Adaptive Cruise Control: Driver Selection of Car-Following Gaps, ITS World Congress, Busan, Korea, November, 2010. 4. Nowakowski, C., D. Cody, J. O Connell and S. E. Shladover, Cooperative Adaptive Cruise Control: Driver Acceptance of Following Gap Settings Less Than One Second, Human Factors and Ergonomics Society 54 th Annual Meeting, San Francisco, September 2010. 5. VanderWerf, J., S.E. Shladover, M.A. Miller and N. Kourjanskaia, Effects of Adaptive Cruise Control Systems on Highway Traffic Flow Capacity, Transportation Research Record No. 1800, Transportation Research Board, Washington DC, 2002, pp. 78-84. 6. Shladover, S; VanderWerf, J.; Miller, MA; Kourjanskaia, N; Krishnan, H, Development and Performance Evaluation of AVCSS Deployment Sequences to Advance from Today'sDriving Environment to Full Automation, California PATH Research Report UCB- ITS-PRR-2001-18. Available online at: http://database.path.berkeley.edu/reports/index.cgi?reqtype=displayrecord&record=619 15

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